Optimal camera selection in array of monitoring cameras

ABSTRACT

Technologies are generally described for automatically optimizing an efficiency of camera placement, numbers, and resolution in multi-camera monitoring and surveillance applications. In some examples, a fraction of a total area may be monitored at a higher resolution than the rest. Employing techniques such as combinatorial state Viterbi technique or combinatorial state trellis technique, a minimum number of cameras that provide the coverage at the needed resolution may be selected. Similarly, a number of points may be covered with at least a predefined number of cameras. For example, a subject of interest may be tracked in a public area, where specific camera(s) may be used to image the subject&#39;s face at a higher resolution than the background.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is the U.S. National Stage filing under 35 U.S.C §371of PCT Application Serial No. PCT/CA2013/050260 filed on Mar. 28, 2013,which claims the benefit under 35 U.S.C §365 (c) of U.S. ProvisionalApplication 61/618,925 filed on Apr. 2, 2012. The disclosures of the PCTApplication and Provisional Application are hereby incorporated byreference in their entireties.

BACKGROUND

This application claims priority to U.S. Provisional Application Ser.No. 61/618,925 filed on Apr. 2, 2012. The provisional application ishereby incorporated by reference in its entirety.

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

In video surveillance applications such as those in subway stations,stores, and airports, areas of importance may need coverage by a networkof camera sensors. In some applications higher resolution images (zooms)may be needed for specific areas. For example, if a subject is beingtacked in a subway station, higher resolution on the face may be neededas opposed to images of the subject's body so the subject can beadequately identified. Higher resolutions may be obtained by settingspecific cameras on higher resolutions. Use of high resolution camerasfor all applications may not only result in prohibitively high cost, butalso increase load on system resources such as bandwidth, storagecapacity, and comparable resources.

In systems where a select number of cameras are high resolution, asurveillance operator may be able select suitable cameras andresolutions for an area such that the number of cameras and the errorbetween the assigned and desired resolutions are minimizedsimultaneously. However, if the number of cameras or the area to becovered is large, the task may become too complicated to be resolvedmanually.

SUMMARY

The present disclosure generally describes methods, apparatus, systems,devices, and/or computer program products related to automaticallyoptimizing the efficiency of camera placement, numbers, and resolutionfor a multi-camera monitoring and surveillance application.

According to some examples, methods for automatically optimizing anefficiency of camera placement, numbers, and resolution in amulti-camera monitoring environment are described. Example methods mayinclude determining a maximum resolution matrix, V, where each element,v_(i,j), of the V represents a maximum resolution with which a camerac_(i) is capable to monitor a point p_(j) in the multi-cameraenvironment; receiving a desired resolution vector, Res_(des), whereeach element of the Res_(des) represents a desired resolution for eachpoint; and evaluating the elements of the V in view of the Res_(des) todetermine an optimal camera and resolution selection taking intoconsideration a cost function, where the cost function includes at leastan error in a resolution assigned to each point.

According to other examples, a computing device operable toautomatically optimize an efficiency of camera placement, numbers, andresolution in a multi-camera monitoring environment is described. Thecomputing device may include a memory configured to store instructions;an input device configured to receive a desired resolution vector,Res_(des), where each element of the Res_(des) represents a desiredresolution for each point in the multi-camera environment; and aprocessor. The processor may be configured to determine a maximumresolution matrix, V, where each element, v_(i,j), of the V represents amaximum resolution with which a camera c_(i) is capable to monitor apoint p_(j) in the multi-camera environment; and evaluate the elementsof the V in view of the Res_(des) to determine an optimal camera andresolution selection taking into consideration a cost function, wherethe cost function includes at least an error in a resolution assigned toeach point.

According to further examples, a method for optimal camera selection inarray of cameras for monitoring and surveillance applications isdescribed. An example method may include determining a plurality ofresolutions associated with a plurality of cameras defined for intervalsalong a linear axis; receiving information associated with points on theintervals and desired resolutions for the points; forming acombinatorial state trellis, where each level represents a pointaccording to a linear order of the points and possible combinations ofcamera resolutions covering the point are listed as states on acorresponding level; and evaluating optimal paths through the levelswhile obeying resolution constraints in each path that is traversed inthe trellis until a survival path is determined.

According to further examples, a computing device for optimal cameraselection in array of cameras for monitoring and surveillanceapplications is described. The computing device may include a memoryconfigured to store instructions and a processor. The processor may beconfigured to determine a plurality of resolutions associated with aplurality of cameras defined for intervals along a linear axis; receiveinformation associated with points on the intervals and desiredresolutions for the points; form a combinatorial state trellis, whereeach level represents a point according to a linear order of the pointsand possible combinations of camera resolutions covering the point arelisted as states on a corresponding level; and evaluate optimal pathsthrough the levels while obeying resolution constraints in each paththat is traversed in the trellis until a survival path is determined.

According to some examples, a method for optimal light subset selectionin a lighting array that achieves a desired intensity for an area ofillumination is provided. An example method may include determining aplurality of lighting intensities associated with a plurality of lightsdefined for intervals along a linear axis; receiving informationassociated with points on the intervals and desired lighting intensitiesfor the points; forming a combinatorial state trellis, wherein eachlevel represents a point according to a linear order of the points andpossible combinations of lighting intensities covering the point arelisted as states on a corresponding level; and evaluating optimal pathsthrough the levels while obeying lighting intensity constraints in eachpath that is traversed in the trellis until a survival path isdetermined.

According to other examples, a computing device for optimal light subsetselection in a lighting array that achieves a desired intensity for anarea of illumination is described. The computing device may include amemory configured to store instructions and processor. The processor maybe configured to determine a plurality of lighting intensitiesassociated with a plurality of lights defined for intervals along alinear axis; receive information associated with points on the intervalsand desired lighting intensities for the points; form a combinatorialstate trellis, wherein each level represents a point according to alinear order of the points and possible combinations of lightingintensities covering the point are listed as states on a correspondinglevel; and evaluate optimal paths through the levels while obeyinglighting intensity constraints in each path that is traversed in thetrellis until a survival path is determined.

According to yet other examples, a computer readable storage medium withinstructions stored thereon for executing the above methods at one ormore processors for optimizing an efficiency of camera placement,numbers, and resolution in a multi-camera monitoring environment mayalso be described.

The foregoing summary is illustrative only and is not intended to be anany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The be foregoing and other features of this disclosure will become morefully apparent From the following description and appended claims, takenin conjunction with the accompanying drawings. Understanding that thesedrawings depict only several embodiments in accordance with thedisclosure and are, therefore, not to be considered limiting of itsscope, the disclosure will be described with additional specificity anddetail through use of the accompanying drawings, in which:

FIG. 1 illustrates an example two-dimensional system of a number ofpoints and multi-resolution cameras;

FIG. 2 illustrates a block diagram of an example system employing thegreedy method for optimal camera selection in an array of cameras formonitoring and surveillance applications;

FIG. 3 illustrates an example one-dimensional scenario where people in asubway station are to be covered with cameras having two differentresolutions;

FIG. 4 illustrates an interval covering computation with a solid linesegment that is divided into four sub-intervals;

FIG. 5 illustrates an example one-dimensional single resolution scenarioof six cameras with different coverage areas;

FIG. 6 illustrates another example one-dimensional multi-resolutioncamera configuration with three cameras, where each camera has tworesolutions and four points to be covered;

FIG. 7 illustrates a further example one-dimensional multi-resolutioncamera configuration with four cameras, where each camera has tworesolutions and three points to be covered;

FIG. 8 illustrates a block diagram of an example system employing acombinatorial state Viterbi technique for optimal camera selection in anarray of cameras for monitoring and surveillance applications;

FIG. 9 illustrates a trellis of the combinatorial states trellistechnique when applied to the example scenario of FIG. 7;

FIG. 10 illustrates a general purpose computing device, which may beused to manage optimal camera selection in an array of cameras formonitoring and surveillance applications;

FIG. 11 is a flow diagram illustrating an example method that may beperformed by a computing device such as the computing device in FIG. 10;and

FIG. 12 illustrates a block diagram of an example computer programproduct, all arranged in accordance with a least some embodimentsdescribed herein.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented herein. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe Figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein.

This disclosure is generally drawn, inter alia, to methods, apparatus,systems, devices, and/or computer program products related to automaticoptimization of efficiency of camera placement, numbers, and resolutionfor a multi-camera monitoring and surveillance application.

Briefly stated, technologies are generally provided for automaticallyoptimizing an efficiency of camera placement, numbers, and resolution inmulti-camera monitoring and surveillance applications. In some examples,a fraction of a total area may be monitored at a higher resolution thanthe rest. Employing techniques such as combinatorial state Viterbitechnique or combinatorial state trellis technique, a minimum number ofcameras that provide the coverage at the needed resolution may beselected. Similarly, a number of points may be covered with at least apredefined number of cameras. For example, a subject of interest may betracked in a public area, where specific camera(s) may be used to imagethe subject's face at a higher resolution than the background.

FIG. 1 illustrates an example tow-dimensional system of a number ofpoints and multi-resolutions cameras, arranged in accordance with atleast some embodiments described herein.

Two-dimensional coverage in area surveillance typically assumes that theobjects in the coverage area are seen at a single resolution. This maynot always be the case, however, and multiple resolutions (for example,for facial recognition purposes) may be desirable. A system according tosome examples treats coverage of two-dimensional surveillance withmultiple resolution selection as an NP-complete configuration employsheuristic techniques to select cameras in an array of cameras formonitoring and surveillance applications. In some example scenarios, thetwo-dimensional configuration may be converted to an equivalentone-dimensional configuration without loss of optimality.

As shown in a diagram 100, monitoring/surveillance system according tosome embodiments may include a number of cameras such as cameras C1through C8. The cameras C1 through C8 may be positioned such that anumber of points p1 through p4 are covered at specific resolutions,among other things. Each camera may have its coverage area such as acoverage area 102 for the camera C1, a coverage area 102 for the cameraC1, a coverage area 104 for the camera C2, a coverage area 106 for thecamera C3, a coverage area 108 for the camera C4, a coverage area 110for the camera C5, a coverage area 112 for the camera C6, a coveragearea 114 for the camera C7, and coverage area 116 for the camera C8.

One or more of the cameras in a system according to some examples may besettable to different resolutions, where each resolution may represent alevel of zoom. Thus, higher resolution may provide a more magnifiedimage. Typically, a higher resolution coverage area of a camera may besmaller than the coverage area for a lower resolution as depicted by thecoverage areas for different resolutions (1 through 3) of the camera C1.The cameras may be configured to cover a number of points with specificresolutions. A number of points and a number of cameras are referred toas NoP and NoC, respectively, herein.

The assigned resolutions to cameras may be represented with a vector A,where each element Xi corresponds to a resolution of a camera i. Alength of the vector X is, thus, NoC.X=[X ₁ X ₂ . . . X _(NoC)]  [1]Assuming a maximum resolution value is n_(R), each vector element may beconsidered asXiε{0, 1, 2, . . . , n _(R)}  [2]in a general case, where points and cameras may be distributed over atwo-dimensional area at any location.

Each point may be desired to be covered with a specific resolution.Thus, the decision involves which cameras to turn on and what resolutionto assign to each camera so that the desired resolution is approximatelyprovided for all of the points. Hence, the number of “ON” cameras and anerror in assigned resolution to each point may be minimizedsimultaneously. A system according to embodiments may model thisconfiguration approach as a discrete optimization.

A matrix V, a maximum resolution matrix, may be defined first, V may bean NoC by NoP matrix, where each element of V, v_(i,j), may representthe maximum resolution with which camera i can see point j. If it can bedetermined that a point can be covered by a camera with at mostresolution k, the point may also be covered by the same camera withlower resolutions and cannot be seen with higher resolutions. Thus, theknowledge of the maximum resolution may include the information aboutother resolutions as well. The maximum resolution matrix, V, may be usedto define one of the costs as discussed below.

One of the cost functions may include the error in the resolutionassigned to each point. For example, for computing the cost for point j,the desired resolution of the point j, Res_(desj), may be compared withthe assigned resolution of the point j, Res_(assj), which may be themaximum resolution among all cameras covering the point j. Equivalentlythe assigned resolution may be written as:Res _(assj)(X)=max(X _(cj)),  [3]whereC _(j) ={i,X _(i) ≦V(i,j)}.  [4]Alternatively, following equation may be written using vectorinequalities:Res _(assj)(X)=max(X(X≦V(i,j))).  [5]

Elements of X which are less than or equal to their correspondingelement in j^(th) column of V may be considered and the maximumselected. If a resolution higher than the desired resolution is assignedto a point, the error in the resolution assignment may be considered tobe zero. With the above listed definitions, the camera selection may bemodeled as a discrete optimization. Thus, the number of “ON” camerascost and the resolution error cost may be linearly combined with aparameter λ to determine a total cost. λ is a factor which determines animportance of one part of the cost function over another Part.Subsequently, the total cost may be minimized using:

$\begin{matrix}{{{\min_{X}{C(X)}} \equiv {\min_{X}\left( {{I_{0}(X)} + {\lambda{\sum\limits_{j = 1}^{NoP}\;\left( {0,{{Res}_{desj} - {{Res}_{assj}(X)}}} \right)}}} \right)}},} & \lbrack 6\rbrack \\{{where}\mspace{14mu}{the}\mspace{14mu}{resolution}\mspace{14mu}{cost}\mspace{14mu}{is}} & \; \\{{{Resolution}\mspace{14mu}{cost}} = {\sum\limits_{j = 1}^{NoP}\;{{\max\left( {0,{{Res}_{desj} - {{Res}_{assj}(X)}}} \right)}.}}} & \lbrack 7\rbrack\end{matrix}$

Where X=[X₁ X₂ . . . X_(NoC)] and I_(o)(X) provide the number ofnon-zero elements of vector X. The max function may be used to imposethat if a higher resolution is assigned to a point, the resolution erroris zero.

Changing λ may have a substantial effect on the solution. For example,if λ is selected large enough, the above described technique may tend tominimize the resolution cost approximately regardless of the first cost.If one cost is not preferred to another, both costs may be normalized totheir maximum value and λ selected to be 1. If the number of cameras isless than the number of points, then the maximum value of I_(o)(X) maybe NoC. IF the number of cameras is not less than the number of points(i.e. NoP<NoC), the approach may be applied with NoP cameras with themaximum value of I_(o)(X) being min(NoC, NoP) in this case. The maximumvalue of the resolution cost may be obtained when the assignedresolutions are zero:

$\begin{matrix}{{\max\left( {{Resolution}\mspace{14mu}{cost}} \right)} = {\sum\limits_{j = 1}^{NoP}\;{{Res}_{desj}.}}} & \lbrack 8\rbrack\end{matrix}$

Next, a minimization may be performed:

$\begin{matrix}{\min_{X}{\left( {\frac{I_{0}(X)}{\min\left( {{NoC},{NoP}} \right)} + {\lambda\frac{\sum\limits_{j = 1}^{NoP}\;\left( {0,{{Res}_{desj} - {{Res}_{assj}(X)}}} \right)}{\sum\limits_{j = 1}^{NoP}\;{Res}_{desj}}}} \right).}} & \lbrack 9\rbrack\end{matrix}$The optimization is N-P complete. Thus, a greedy technique may beemployed for computing the optimization in some examples.

FIG. 2 illustrates a block diagram of an example system employing thegreedy method for optimal camera selection in an array of cameras formonitoring and surveillance applications, arranged in accordance with atleast some embodiments described herein.

In the optimization discussed above, a greedy method may be employed ina resolution-first camera assignment approach. Employing the greedymethod may provide. Acceptable results with relatively high speed andlow complexity, thus avoiding an increased need for computational systemresources. In the example implementation discussed below, more weightmay be given to the resolution cost rather than the number of ON camerascost.

A greedy method according to some embodiments may minimize the totalcost function iteratively. At each iteration, a camera may be selectedand a resolution assigned to the camera such that the selected cameraand its assigned resolution impose a largest reduction on the costfunction among other choices. The iterations may continue until no otherselections (camera or resolution) decrease the cost function, forexample.

A diagram 200 illustrates how the iterative process may be performed.The inputs of the iterative process may include Res_(des), an array ofdesired resolutions of target points; λ, a factor which determines theweight of one part of cost function over another part; and V, a matrix212 which represents the maximum resolution that a camera observes apoint.

For each camera, the best resolution may be selected through bestresolution selection blocks 214, 216. For brevity two such blocks areshown, but one block for each camera may be employed in an exampleimplementation. The best resolution for each camera may be the one thatdecreases the cost function more than other resolutions. In order toselect the best resolution for a camera, the total cost function may becomputed for all assignable resolutions. Then, the minimum value ofcomputed costs and a corresponding resolution may be provided to aninput of a cost analyzer block 218. The cost analyzer block 218 maydetermine the minimum cost among input costs (Cost₁, . . . Cost_(NoC))and compare the minimum cost with the cost of previous iteration(PreCost). In response to determining substantially no differencebetween the current minimum cost and the previous cost, the costanalyzer block 218 may terminate the iterations and provide a “Ready”signal. Else, the minimum cost may be fed to the best resolutionselection blocks 214, 216 for the next iteration.

FIG. 3 illustrates an example one-dimensional scenario where people in asubway station are to be covered with cameras having two differentresolutions, arranged in accordance with at least some embodimentsdescribed herein.

In some examples, a special case of one-dimensional configurations,which have specific applications, may be considered. In such scenarios,the optimal solution may be achieved using combinatorial state Viterbitechnique in some embodiments.

A diagram 300 depicts an example scenario for one-dimensionalconfigurations. In the example scenario of the diagram 300, objects(people 333 and 335) in a subway station may be on either side of atrain 334. As illustrated in a diagram 300, cameras 332 and 336 oneither side of the tunnel may monitor the people 333 and 335 on theirrespective sides. An approximation may be made that people 333 and 335are roughly standing on a line making the optimization a linear one.

In the example scenario, the cameras 332 and 336 may have two differentresolutions (e.g., normal coverage are and higher resolution coveragearea available via zoom). If a line can be fitted through the points(the people being monitored), for example based on the least squarederror, and the points projected on the line such that the V matrix doesnot change, the optimal selection for the two-dimensional configurationmay be determined by computing the one-dimensional configuration withoutloss of optimality. Thus, the two-dimensional approach may be simplifiedto a one-dimensional approach of intervals (parts of the objects on aline) which may be desired to be covered with different resolutions. Inother embodiments, a point covering approach may be employed in theone-dimensional configuration.

FIG. 4 illustrates an interval covering computation with a solid linesegment that is divided into four sub-intervals, arranged in accordancewith at least some embodiments described herein.

Considering that in the multi-resolution camera assignment, a number ofintervals with predetermined resolutions are to be covered, thecomputation may be converted to a technique in which instead ofintervals, a number of points are covered, in some example embodiments.Thus, each interval may be broken down into a number of sub-intervalsand a point may be considered as the representative of eachsub-interval. Then the optimization may be performed for the points.

The interval of the coverage area of i^(th) camera sensor may berepresented by [a_(i), b_(i)]. The intervals along the line 440 may beconverted to points as follows. The beginning (I_(int)) and the end ofinterval (u_(int)) may be considered and the beginnings and ends of thecameras coverage boundaries of any resolutions sorted.

a diagram 400 depicts cameras C1 and C2 observe an interval underconsideration 445 with high resolution coverages 442 and 444,respectively. The interval under consideration 445 is depicted as solidline segment and is segmented to four sub-intervals. As shown on thediagram 400, I_(int) may be I₀ and u_(int) may be I₄ in the examplescenario. All points on each sub-interval may be covered with the samecameras and resolutions. Thus, a single point may be considered as arepresentative of each sub-interval.

FIG. 5 illustrates an example one-dimensional single resolution scenarioof six cameras with different coverage areas, arranged in accordancewith at least some embodiments described herein.

A diagram 500 illustrates an example scenario with a number of cameras(C1 through C6) covering a line 546, where the cameras may have overlapin their coverage areas. As shown in the diagram 500, there are sixcameras C1 through C6 with different coverage areas (a₁, b₁; a₂, b₂; . .. a₆, b₆) and also five points (p₁ through p₅) to be covered. In theexample scenario, no resolution is assigned to the points. Therefore,the cameras' resolutions do not change and the aim is to cover thepoints with minimum number of cameras. Thus, a discrete (combinatorial)optimization technique may be applied. An assumption may be made thatthe points may be on a substantially straight line and the coverage ofeach camera may be defined with an interval. Thus, the points, p_(i),represent objects or parts of the objects on a real number axis.

Taking advantage of the order among the points on the real number axisand without loss of generality, the computation may begin from the mostleft-hand side point (p₁). Starting from the point, p₁, a camera withthe maximum coverage on the real axis may be selected after the p₁location. This selection may be considered as equivalent to theselection of the interval which contains the point and has the largestb_(i) (the end of the camera coverage interval). By selecting theinterval with the point p₁ and the largest b_(i), any possible optimalselection is not lost because after this selection, any other intervalmay be selected without any restriction. Thus, optimality is not lost.

On the other hand, among the cameras which cover the desired point, acamera which has the maximum coverage or (in other words the greatestlikelihood to cover other points) may be selected. By performing theselection of the initial camera as described, the computation may movetoward selecting the optimal cameras. For the next point, adetermination may be made whether the next point is covered or not. Ifthe next point is covered, the computation may move to the subsequentpoint. If the next point is not covered, the selection of the firstpoint may be repeated for the next point as well. The selection processmay be iteratively repeated until all points are covered.

The above-described approach may be applied in reverse directionstarting at the most right-hand side point (p₅) as well selectingcameras with maximum coverage on the real axis from the most right-handside point toward the left.

FIG. 6 illustrates another example one-dimensional multi-resolutioncamera configuration with three cameras, where each camera has tworesolutions and four points to be covered, arranged in accordance withat least some embodiments described herein.

As mentioned previously, the one-dimensional single resolution scenariois a generalization of the multi-resolution configuration and a specialcase of the two-dimensional configuration. Therefore, the same costfunctions as discussed in the two-dimensional case may be minimized. Asin the two-dimensional configuration each camera may be set to differentresolutions but one at a time in case of the one-dimensionalmulti-resolution camera configuration. In some examples, differentresolutions of single camera may be considered as distinct cameras withdifferent coverage areas or as intervals with different lengths. Adiagram 600 shows an example configurations with three cameras C1, C2,and C3.

In the diagram 600, the cameras C1, C2, and C3 have, each, a lowresolution coverage and a high resolution coverage (652, 654, 656,respectively). Points p1 through p4 to be covered are lined along theline 650. While the point p1 is in the low resolution coverage area ofthe camera C1, the points p2, p3, and p4 are in overlapping coverageareas. For example, the point p2 is in an overlap area 653 that iscovered by the high resolution coverage 652 of the camera C1 and the lowresolution coverage of the camera C2. The point p3 is in an area coveredby the low resolution coverages of the camera C2 and C3. The point p4 isin an overlap area 655 covered by the low resolution coverage of thecamera C2 and by the high resolution coverage 656 of the camera C3.

Optimizing the illustrated example configuration, the same cost function[7] as in the two-dimensional case may be used with the difference that:Resolutions cost=L*Res _(desint)−Σ_(i=1) len _(i) *Res _(assi)=Σ_(i=1)len _(i)*(Res _(desint) −Res _(assi)),  [10]where L and Res_(desint) are the length and desired resolution of theoriginal interval respectively. Also, len_(i) and Res_(assi) are thelength of and the assigned resolution to each interval respectively.Minimizing the above cost may be substantially equal to minimizing theterm in the parentheses for every value of i. Since the cost is a linearcombination of sub-costs with positive coefficients, in order tominimize the overall cost, the cost of each sub-interval may beindependently minimized.

Among the points which are desired to be seen with the same resolutionand are similar in terms of cameras that cover them, one point may beconsidered and the others removed. If one of them is covered with aminimum error, the others may be covered with the same error becausetheir situations may be identical and if a camera is assigned to one ofthem, others may be seen with the same camera and resolution. Theproperty for removing some points may be used in this example scenarioand the points in each interval may be removed except one. The selectedpoint may be the representative of its respective interval and theselection may be arbitrary. As a non-conflicting choice, the middlepoint of each interval may be selected as its representative, forexample. It should be noted that when all of the intervals areconsidered, the sub-interval length may be multiplied by the desiredresolution in order to count a length to the total resolution cost.Thus, the point covering technique may include selection of a middlepoint for each sub-interval and the cost may be the desired resolutionof that sub-interval multiplied by the length of it.

FIG. 7 illustrates a further example one-dimensional multi-resolutioncamera configuration with four cameras, where each camera has tworesolutions and three points to be covered, arranged in accordance witha least some embodiments described herein.

An example scenario is shown in a diagram 700 with four multi-resolutioncameras C1 through C4. According to the example scenario, the cameraresolutions may vary between 1 and 4 and the three points p1, p2, andp3, may be desired to be covered with resolutions 1, 4, and 1,respectively. To provide optimum coverage and desired resolutions to thepoints lines up along the line 760, a combinatorial state trellistechnique may be employed as discussed in more detail below inconjunction with FIG. 8 and FIG. 9.

The result of the optimization may provide a low resolution coverage 762of the camera C1 to p1, a high resolution coverage 764 of the camera C2to p2, and low resolution coverage 766 of the camera C3 to p3. Theoptimization may select resolution zero for the cameras 3 and 4 meaningthese cameras are not selected and may be turned off.

FIG. 8 illustrates a block diagram of an example system employing acombinatorial state Viterbi technique for optimal camera selection in anarray of cameras for monitoring and surveillance applications, arrangedin accordance with at least some embodiments described herein.

The example block diagram of the combinatorial state Viterbi techniqueshown in a diagram 800 includes state generators 882, 884, which maytake a V matrix 886 as input and generate combinations of cameras andresolutions (i.e. states) with which a target point may be observed. Thestates may be provided from the state generators 882, 884 tocombinatorial state Viterbi implementers 874, 876, which with somemodifications to the Viterbi algorithm, may select the best path to eachstate and calculate the cost based on the selected path. While twogenerators and combinatorial state Viterbi implementers are shown forillustration purposes, a plurality of those blocks may be employed inpractical implementations, for example, one for each column for the Vmatrix 886. The combinatorial state Viterbi implementers 874, 876 mayable receive a Res_(des) vector 872 representing desired resolutions forthe different cameras as input. A λ parameter defining weighting amongthe different cameras may also be provided to the combinatorial stateViterbi implementers 874, 876. A minimum cost path finder block 878 maydetermine a state among the states of the last branch that results inthe minimum cost.

In some examples, an exhaustive search which considers all eligible(consistent) cases, computes that cost of each case, and chooses thecase with the lowest cost may be employed in selecting optimal cameraconfiguration. In an example multi-resolution configuration, twodifferent exhaustive search approaches may be employed. First, approach(exhaustive search on the points) may consider the points and possiblescenarios of camera assignment to the points. After determining thepossible (not necessarily eligible) scenarios of cameras and theirresolutions, the resolution consistency may be examined and scenarioswhich violate the consistency may be omitted. Among the remainingscenarios, a scenario with the lowest cost may be selected. When acamera is considered for a point, the camera may be set on resolutionzero meaning that camera has not been selected (it is off) or set on aresolution that cannot see the point to which the camera is assigned to.

Another approach (exhaustive search on the cameras) may consider thecameras and test possible resolutions for each camera. In this casethere may not be a need to check the consistency because each camera isconsidered once an assigned a resolution (including resolution zero).Depending on the number of points and cameras, the two approaches maydiffer in time/computational complexity.

In further embodiments, a Viterbi technique may be applied to awell-defined trellis to determine the optimal configuration. An optimallow complexity combinational-state trellis that takes resolutionconstraint into consideration may be used. In the trellis, a level maybe considered for each point and possible combinations of camerasresolutions covering a point may be listed as states on the level.Resolution constraint may be obeyed in each path that is traversed inthe trellis. In this approach, transitions may be formed from one level(point) to the next level, while resolution consistency is followed forthe common cameras corresponding to the start and end states of eachtransition. It should be noted that the coverage area of each camera ona real axis is contiguous and includes one segment. Thus, when examiningfor resolution consistency in a path, the resolution consistency may beexamined from one level to the next without having to examine previouslevels of the path because when the current camera is not seen in theprevious level, it means that the current level is the first time thatthe camera is selected in the examined path.

The resolution consistency for a camera may also be not needed to beexamined in the future states on the path where the camera is notpresent. Because there is the possibility of making a decision about thefuture branches to a state and selecting an optimum one (which has theminimum cost) without considering the past and the future of the path,the Viterbi technique may be applied directly to find the survival pathin a trellis. The total cost of each transition may be defined to be alinear combination of the number of cameras and resolution costs withequal weight, as discussed previously.

Camera resolution consistency in transitions from one level to the nextmay allow transition which begins from one resolution of a camera andends in a different resolution of the same camera. There may be someexceptions in the case of resolution zero (when camera is not selected).Transitions from resolution zero to any other resolutions and vice versamay be allowed subject to the following restrictions.

Transition from a non-zero resolution to a zero resolution may beallowed if the maximum possible resolution in the next level is lessthan the resolution in the start of the transition. For example, theresolution of the camera corresponding to the start state of thetransition may be 2. This means that the camera needs to be set to havea resolution of 2 in the current path. If the camera covers the nextlevel (point) with a resolution of 2 or more, the camera cannot betransitioned to resolution zero because the camera can also see the nextpoint with a resolution of 2 and choice of resolution zero violates theresolution consistency. However, if the maximum available resolution inthe next level is 1, a transition may be made from resolution 2 to zero.This scenario may occur when the range of coverage of a camera with aresolution of 2 ends at the beginning of the branch in the previouslevel and it can only see the next point when set on resolution 1.

Another constraint may be the dual of the above-discussed constraint andmay occur when a transition is attempted from resolution zero to anon-zero one. Such a transition may be performed if the maximumresolution in the previous level is less than the non-zero resolution ofthe desired state.

When a transition is made from a non-zero resolution (x) to zero, theindication is that the coverage interval of the camera with resolution xhas ended before the next level and there may not be a transition to anynon-zero resolutions in the future of this path. This means thatnon-zero to zero and then zero to non-zero transition may not occuranywhere in a path. Thus, such transitions may be monitored for eachpath and variable (e.g., a “check” variable) may be set to predefinedvalue when such a transition happens and the branch selected as thebranch with lowest cost that enters the current state.

In some examples, keeping track of such transitions (from non-zero tozero) once for all paths (equivalently once for each camera) may besufficient and not for every single path. If the check variable is setto the predefined value and the resolution of the current state of thecurrent path is zero, then the transition from non-zero to zero may needto necessarily occur once in the current path. Thus, the check variablemay be set to the predefined value if the transition branch fromnon-zero to zero is selected as the survival path going to the statewith resolution zero in the next level.

Embodiments are not limited to the above-discussed configurations ortechniques. Other optimization techniques and camera configurations withmulti-resolution, multidirectional (e.g. PTZ) cameras may also beselected for optimal configuration using the principles discussedherein. Furthermore, the principles discussed here may be applied tolighting systems for generating an optimal light subset in a lightingarray that achieves a desired intensity for an area of illumination.

FIG. 9 illustrates a trellis of the combinatorial state trellistechnique when applied to the example scenario of FIG. 7, arranged inaccordance with at least some embodiments described herein.

A depiction of a trellis for the combinatorial state trellis techniquethat may be applied to the example scenario of FIG. 7 is shown in adiagram 900. For brevity, some of the transitions including the optimalone to the third level are depicted on the diagram 900. The techniquedoes not decide among different resolutions of a camera in finding theoptimal configuration. The optimal configuration path is shown with abold line as the survival path. The survival path starts with theoptions 902 for p1 and C1, where C1 is determined to be ON. Next,different configurations of C1 and C2 (options 904) are considered forp2 and the combination of C1 having the lowest resolution (1) and C2having the highest resolution (4) is selected). Subsequently, options906 for all four cameras and p3 are considered leading to thedetermination that the cameras C2, C3, and C4 are not needed for thispoint. Thus, the survival path is (1)→(1,4)→(1,0,0,0).

By modifying that states and V matrix, the same technique may be appliedto the k-coverage in multidirectional camera sensors in one-dimensionalconfigurations, a one-dimensional scenario of pan-tilt-zoom (PTZ)cameras, or a one-dimensional case of multi-resolution/multidirectionalcameras. For example for a multidirectional scenario, differentcombinations of directions may be considered as states on each branch ofthe trellis. Direction 0 for each camera may also be consideredrepresenting when the camera is off or a current point cannot be seenwith the selected direction of the camera. In the directional camerascenario, the V matrix may be changed to a direction matrix (D) suchthat each component d_(i,j) shows the direction in which camera i cansee point j. As another example for a multi-resolution/multidirectionalscenario in one dimension, combinatorial states to two component vectorsmay be used where the first component may show the direction and thesecond component may show the maximum resolution with which each cameracan see a current point.

FIG. 10 illustrates a general purpose computing device, which may beused to mange optimal camera selection in an array of cameras formonitoring and surveillance applications, arranged in accordance with atleast some embodiments described herein.

For example, the computing device 1000 may be used as a server, desktopcomputer, portable computer, smart phone, special purpose computer, orsimilar device such as a controller at a utility control center or acontroller at a micro grid. In an example basic configuration 1002, thecomputing device 1000 may include one or more processors 1004 and asystem memory 1006. A memory bus 1008 may be used for communicatingbetween the processor 1004 and the system memory 1006. The basicconfiguration 1002 is illustrated in FIG. 10 by those components withinthe inner dashed line.

Depending on the desired configurations, the processor 1004 may be anytype, including but not limited to a microprocessor (μP), amicrocontroller (μC), a digital signal processor (DSP), or anycombination thereof. The processor 1004 may include one more levels ofcaching, such as a cache memory 1012, one or more processor cores 1014,and registers 1016. The example processor cores 1014 may (each) includean arithmetic logic unit (ALU), a floating point unit (FPU), a digitalsignal processing core (DSP Core), or any combination thereof. Anexample memory controller 1018 may also be used with the processor 1004,or in some implementations the memory controller 1018 may be an internalpart of the processor 1004.

Depending on the desired configuration, the system memory 1006 may be ofany type including but not limited to volatile memory (such as RAM),non-volatile memory (such as ROM, flash memory, etc.) or any combinationthereof. The system memory 1006 may include an operating system 1020,one or more applications 1022, and program data 1024. The application1022 may include an optimization module 1026, which may be an integralpart of the application 1022 or a separate application on its own. Theoptimization module 1026 may perform optimal camera selection in anarray of cameras for monitoring and surveillance applications, asdescribed herein. The program data 1024 may include, among other data,data 1028 related to camera positions, resolutions, or the like, asdescribed herein.

The computing device 1000 may have additional features or functionality,and additional interfaces to facilitate communications between the basicconfiguration 1002 and any desired devices and interfaces. For example,a bus/interface controller 1030 may be used to facilitate communicationsbetween the basic configuration 1002 and one or more data storagedevices 1032 via a storage interface bus 1034. The data storage devices1032 may be one or more removable storage devices 1036, one or morenon-removable storage devices 1038, or a combination thereof. Examplesof the removable storage and the non-removable storage devices includemagnetic disk devices such as flexible disk drives and hard-disk drives(HDD), optical disk drives such as compact disk (CD) drives or digitalversatile disk (DVD) drives, solid state drives (SSD), and tape drivesto name a few. Example computer storage media may include volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information, such as computer readableinstructions, data structures, program modules, or other data.

The system memory 1006, the removable storage devices 1036 and thenon-removable storage devices 1038 are examples of computer storagemedia. Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD), solid state drives, or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by the computingdevice 1000. Any such computer storage media may be part of thecomputing device 1000.

The computing device 1000 may also include an interface bus 1040 forfacilitating communication from various interface devices (e.g., one ormore output devices 1042, one or more peripheral interfaces 1044, andone or more communication devices 1066) to the basic configuration 1002via the bus/interface controller 1030. Some of the example outputdevices 1042 include a graphics processing unit 1048 and an audioprocessing unit 1050, which may be configured to communicate to variousexternal devices such as a display or speakers via one or more A/V ports1052. One or more example peripheral interfaces 1044 may include aserial interface controller 1054 or a parallel interface controller1056, which may be configured to communicate with external devices suchas input devices (e.g., keyboard, mouse, pen, voice input device, touchinput device, etc.) or other peripheral devices (e.g., printer, scanner,etc.) via one or more I/O ports 1058. An example communication device1066 includes a network controller 1060, which may be arranged tofacilitate communications with one or more other computing devices 1062over a network communication link via one or more communication ports1064. The one or more other computing devices 1062 may include servers,camera controller, and comparable devices.

The network communication link may be one example of a communicationmedia communication media may typically be embodied by computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. A “modulateddata signal” may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.By way of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), microwave,infrared (IR) and other wireless media. The term computer readable mediaas used herein may include both storage media and communication media.

The computing device 1000 may be implemented as a part of a generalpurpose or Specialized server, mainframe, or similar computer thatincludes any of the above functions. The computing device 1000 may alsobe implemented as a personal computer including both laptop computer andnon-laptop computer configurations.

Example embodiments may also include methods for maintaining applicationperformances upon transfer between cloud servers. These methods can beimplemented in any number of ways, including the structures describedherein. One such way may be by machine operations, of devices of thetype described in the present disclosure. Another optional way may befor one or more of the individual operations of the methods to beperformed in conjunction with one or more human operators performingsome of the operations while other operations may be performed bymachines. These human operators need not be collocated with each other,but each can be only with a machine that performs a portion of theprogram. In other embodiments, the human interaction can be automatedsuch as by pre-selected criteria that may be machine automated.

FIG. 11 is a flow diagram illustrating an example method that may beperformed by a computing device such as the computing device in FIG. 10,arranged in accordance with at least some embodiments described herein.

Example methods may include one or more operations, functions or actionsas illustrated by one or more blocks 1122, 1124, 1126, and/or 1128. Theoperations described in the blocks 1122 through 1128 may also be storedas computer-executable instructions in a computer-readable medium suchas a computer-readable medium 1120 of a computing device 1110.

An example process for optimal camera selection in array of cameras formonitoring and surveillance applications may begin with block 1122,“DETERMINE A MAXIMUM RESOLUTION MATRIX, V. EACH ELEMENT, V_(I,J),REPRESENTING A MAXIMUM RESOLUTION WITH WHICH A CAMERA C₁ IS CAPABLE TOMONITOR A POINT P_(J)”, where a maximum resolution matrix such as matrixV (212) may be determined by a processor of a controller for amonitoring application.

Block 1122 may be followed by block 1124, “RECEIVED A DESIRED RESOLUTIONVECTOR, RES_(DES), EACH ELEMENT OF THE RES_(DES) REPRESENTING A DESIREDRESOLUTION FOR EACH POINT P_(J)”, where desired resolution informationmay be received, for example, from a user or an automated system such asone that aims to perform facial recognition in a public area.

Block 1124 may be followed by block 1126, “EVALUATE THE ELEMENTS OF V INVIEW OF RES_(DES) TO DETERMINE AN OPTIMAL CAMERA AND RESOLUTIONSELECTION TAKING INTO CONSIDERATION A COST FUNCTION”, where theresolutions and cameras may be evaluated through an iterative greedytechnique or a combinatorial-state trellis computation (employingViterbi technique) by a best resolution selection module 216, forexample.

Block 1126 may be followed by block 1128, “MINIMIZE COST FUNCTION”,where the cost function may be minimized by a cost analyzer block 218,for example. The evaluation techniques may also be applied in lightingapplications, where selected areas may be associated with desiredlighting intensities from an array of lights.

The blocks included in the above described process are for illustrationpurposes. Optimal camera selection in array of cameras for monitoringand surveillance applications may be implemented by similar processeswith fewer or additional blocks. In some embodiments, the blocks may beperformed in a different order. In some other embodiments, variousblocks may be eliminated. In still other embodiments, various blocks maybe divided into additional blocks, or combined together into fewerblocks.

FIG. 12 illustrates block diagram of an example computer programproducts, arranged in accordance with at least some embodimentsdescribed herein.

In some embodiments, as shown in FIG. 12, the computer program product1200 may include a signal bearing medium 1202 that may also include oneor more machine readable instructions 1204 that, when executed by, forexample, a processor, may provide the functionality described herein.Thus, for example, referring to the processor 1004 in FIG. 10, anoptimization module 1026 executed on the processor 1004 may undertakeone or more of the tasks shown in FIG. 12 in response to theinstructions 1204 conveyed to the processor 1004 by the signal bearingmedium 1202 to perform actions associated with optimal camera selectionin array of cameras for monitoring and surveillance applications asdescribed herein. Some of those instructions may include, for example,instructions for determining a maximum resolution matrix, V, eachelement, v_(i,j), representing a maximum resolution with which a camerac_(i) is capable to monitor a point p_(j); receiving a desiredresolution vector, Res_(des), each element of the Res_(des) representinga desired resolution for each point p_(j); evaluating the elements of Vin view of Res_(des) to determine an optimal camera and resolutionselection taking into consideration a cost function; minimizing the costfunction according to some embodiments described herein.

In some implementations, the signal bearing medium 1202 depicted in FIG.12 may encompass a computer-readable medium 1206, such as, but notlimited to, a hard disk drive, a solid state drive, a Compact Disc (CD),a Digital Versatile Disk (DVD), a digital tape, memory, etc. In someimplementations, the signal bearing medium 1202 may encompass arecordable medium 1208, such as, but not limited to, memory, read/write(R/W) CDs, R/W DVDs, etc. In some implementations, the signal bearingmedium 1202 may encompass a communications medium 1210, such as, but notlimited to, a digital and/or an analog communication medium (e.g., afiber optic cable, a waveguide, a wired communications link, a wirelesscommunication link, etc.). Thus, for example, the computer programproduct 1200 may be conveyed to one or more modules of the processor1004 of FIG. 10 by an RF signal bearing medium, where the signal bearingmedium 1202 is conveyed by the wireless communications medium 1210(e.g., a wireless communications medium conforming with the IEEE 802.11standard).

According to some examples, methods for automatically optimizing anefficiency of camera placement, numbers, and resolution in amulti-camera monitoring environment are described. Example methods mayinclude determining a maximum resolution matrix, V, where each element,v_(i,j), of the v represents a maximum resolution with which a camerac_(i) is capable to monitor a point p_(j) in the multi-cameraenvironment; receiving a desired resolution vector, Res_(des), whereeach element of the Res_(des) represents a desired resolution for eachpoint; and evaluating the elements of the V in view of the Res_(des) todetermine an optimal camera and resolution selection taking intoconsideration a cost function, where the cost function includes at leastan error in a resolution assigned to each point.

According to other examples, the methods may further include evaluatingthe elements of the V in view of a weighting parameter for the costfunction; linearly combining a number of selected cameras' cost and theerror in the resolution cost with the weighting parameter to determine atotal cost; and/or minimizing the total cost to determine an optimalselection of cameras and resolutions to monitor predefined points in themulti-camera monitoring environment with desired resolutions. Themethods may also include employing a greedy technique to minimize thetotal cost iteratively; selecting a camera and assigning a resolution tothe selected camera such that the selected camera and the assignedresolution impose a largest reduction on the total cost at eachiteration; and continuing the iterations until no other camera orresolution selection decreases the cost function.

According to further examples, the methods may include receiving theresolutions defined for intervals along a linear axis; and selecting thepoints to represent the intervals. The methods may further includeselecting the points as middle points of each interval and/or computingthe cost for each interval as a product of a desired resolution for theinterval and a length of the interval. Each resolution may represent alevel of zoom. The cameras may include one of: a single resolutioncamera, a multi-resolution camera, a multidirectional camera, amulti-resolution/multidirectional camera, and pan-tilt-zoom (PTZ)camera.

According to other example, a computing device operable to automaticallyoptimize an efficiency of camera placement, numbers, and resolution in amulti-camera monitoring environment is described. The computing devicemay include a memory configured to store instructions; an input deviceconfigured to receive a desired resolution vector, Res_(des), where eachelement of the Res_(des) represents a desired resolution for each pointin the multi-camera environment; and a processor. The processor may beconfigured to determine a maximum resolution matrix, V, where eachelement, v_(i,j), of the V represents a maximum resolution with which acamera c_(i) is a capable to monitor a point p_(j) in the multi-cameraenvironment; and evaluate the elements of the V in view of the Res_(des)to determine an optimal camera and resolution selection taking intoconsideration a cost function, where the cost function includes at leastan error in a resolution assigned to each point.

According to some examples, the processor may also be configured toevaluate the elements of the V in view of a weighting parameter for thecost function; linearly combine a number of selected cameras' cost andthe error in the resolution cost with the weighting parameter todetermine a total cost; minimize the total cost to determine an optimalselection of cameras and resolutions to monitor predefined points in themulti-camera monitoring environment with desired resolutions; and/oremploy a greedy technique to minimize the total cost iteratively.

According to yet other examples, the processor may be further configuredto select a camera and assign a resolution to the selected camera suchthat the selected camera and the assigned resolution impose a largestreduction on the total cost as each iteration; and continue theiterations until no other camera or resolution selection decreases thecost function. The processor may also receive the resolutions definedfor intervals along a linear axis and select the points to represent theintervals. The processor may further select the points as middle pointsof each interval and compute the cost for each interval as a product ofs desired resolution for the interval and a length of the interval. Eachresolution may represent a level of zoom. The cameras may include oneof: a single resolution camera, a multi-resolution camera, amultidirectional camera, a multi-resolution/multidirectional camera, anda pan-tilt-zoom (PTZ) camera.

According to further examples, a method for optimal camera selection inarray of cameras for monitoring and surveillance applications isdescribed. An example method may include determining a plurality ofresolutions associated with a plurality of cameras defined for intervalsalong a linear axis; receiving information associated with point on theintervals and desired resolutions for the points; forming acombinatorial state trellis, where each level represents a pointaccording to a linear order of the points and possible combinations ofcamera resolutions covering the point are listed as states on acorresponding level; and evaluating optimal paths through the levelswhile obeying resolution constraints in each path that is traversed inthe trellis until a survival path is determined.

According to some examples, the method may further include formingtransitions from one level to a next level, while following a resolutionconsistency for common cameras corresponding to a start and an end stateof each transition; enabling a transition from a non-zero resolution toa zero resolution in response to a determination that a maximum possibleresolution in the next level is less than a resolution at a start of thetransition; and/or enabling a transition from a zero resolution to anon-zero resolution in response to a determination that a maximumpossible resolution in a previous level is less than a non-zeroresolution of a desired state.

According to other example, the method may also include monitoringtransitions from a non-zero resolution to a zero resolution for eachpath in the trellis; setting a variable to a predefined value when atransition from the non-zero resolution to the zero resolution occurs;and selecting a current branch as a branch with lowest cost that entersa current state. The method may further include defining combinations ofdirections for multidirectional cameras as states on each branch of thetrellis and performing an exhaustive search by: examining all eligiblecombinations, computing a cost of each combination, and selecting acombination with a lowest cost.

According to yet other examples, the method may include evaluating thepoints and possible combinations of camera assignment to the points;upon determining one or more possible camera and resolution combinationsfor each point, examining a resolution consistency; omitting possiblecamera and resolution combinations that violate the resolutionconsistency; and selecting a camera and resolution combination amongremaining camera and resolution combinations. The method may alsoinclude evaluating the cameras and testing possible resolutions for eachcamera without examining a resolution consistency; setting a resolutionfor a camera to zero in response to a determination that the camera isto be turned off of set to e resolution that fails to cover a point towhich the camera is assigned; and/or defining a total cost of eachtransition as a linear combination of a number of cameras and resolutioncosts. The cameras may include one of: a single resolution camera, amulti-resolution camera, a multidirectional camera, amulti-resolution/multidirectional camera, and a pan-tilt-zoom (PTZ)camera.

According to further examples, a computing device for optimal cameraselection in array of cameras for monitoring and surveillanceapplications is described. The computing device may include a memoryconfigured to store instructions and a processor. The processor may beconfigured to determine a plurality of resolutions associated with aplurality of cameras defined for intervals along a linear axis; receiveinformation associated with points on the intervals and desiredresolutions for the points; form a combinatorial state trellis, whereeach level represents a point according to a linear order of the pointsand possible combinations of camera resolutions covering the point arelisted as states on a corresponding level; and evaluate optimal pathsthrough the levels while obeying resolution constraints in each paththat is traversed in the trellis until a survival path is determined.

According to some examples, the processor may be further configured toform transitions from one level to a next level, while following aresolution consistency for common cameras corresponding to a start andan end state of each transition; enable a transition from a non-zeroresolution to a zero resolution in response to a determination that amaximum possible resolution in the next level is less than a resolutionat a start of the transition; and/or enable a transition from a zeroresolution to a non-zero resolution in response to a determination thata maximum possible resolution in a previous level is less than anon-zero resolution of a desired state.

According to other examples, the processor may also be configured tomonitor transitions from a non-zero resolution to a zero resolution foreach path in the trellis; set a variable to a predefined value when atransition from the non-zero resolution to the zero resolution occurs;and select a current branch as a branch with lowest cost that enters acurrent state. The processor may further define combinations ofdirections for multidirectional cameras as states on each branch of thetrellis and perform an exhaustive search by examining all eligiblecombinations, computing a cost of each combination, and selecting acombination with a lowest cost.

According to yet other examples, the processor may evaluate the pointsand possible combinations of camera assignment to the points; upondetermining one or more possible camera and resolution combinations foreach point, examine a resolution consistency; omit possible camera andresolution combinations that violate the resolution consistency; andselect a camera and resolution combination among remaining camera andresolution combinations. The processor may also evaluate the cameras andtesting possible resolutions for each camera without examining aresolution consistency and/or set a resolution for a camera to zero inresponse to a determination that the camera is to be turned off of setto e resolution that fails to cover a point to which the camera isassigned. The processor may further define a total cost of eachtransition as a linear combination of a number of cameras and resolutioncosts. The cameras may include one of: a single resolution camera, amulti-resolution camera, a multidirectional camera, amulti-resolution/multidirectional camera, and a pan-tilt-zoom (PTZ)camera.

According to some examples, a method for optimal light subset selectionin a lighting array that achieves a desired intensity for an area ofillumination is provided. An example method may include determining aplurality of lighting intensities associated with a plurality of lightsdefined for intervals along a linear axis; receiving informationassociated with points on the intervals and desired lighting intensitiesfor the points; forming a combinatorial state trellis, wherein eachlevel represents a point according to a linear order of the points andpossible combinations of lighting intensities covering the point arelisted as states on a corresponding level; and evaluating optimal pathsthrough the levels while obeying lighting intensity constraints in eachpath that is traversed in the trellis until a survival path isdetermined.

According to other examples, a computing device for optimal light subsetselection in a lighting array that achieves a desired intensity for anarea of illumination is described. The computing device may include amemory configured to store instructions and a processor. The processormay be configured to determine a plurality of lighting intensitiesassociated with a plurality of lights defined for intervals along alinear axis; receive information associated with points on the intervalsand desired lighting intensities for the points; form a combinatorialstate trellis, wherein each level represents a point according to alinear order of the points and possible combinations of lightingintensities covering the point are listed as states on a correspondinglevel; and evaluate optimal paths through the levels while obeyinglighting intensity constraints in each path that is traversed in thetrellis until a survival path is determined.

According to yet other examples, a computer readable storage medium withinstructions stored thereon for executing the above methods at one ormore processors for optimizing an efficiency of camera placement,numbers, and resolution in a multi-camera monitoring environment mayalso be described.

There is little distinction left between hardware and softwareimplementations of aspects of systems; the use of hardware or softwareis generally (but not always, that in certain contexts the choicebetween hardware and software may become significant) a design choicerepresenting cost vs. efficiency tradeoffs. There are various vehiclesby which processes and/or systems and/or other technologies describedherein may be effected (e.g., hardware, software, and/or firmware), andthat the preferred vehicle will vary with the context in which theprocesses and/or systems and/or other technologies are deployed. Forexample, if an implementer determines that speed and accuracy areparamount, the implementer may opt for a mainly hardware and/or firmwarevehicle; if flexibility is paramount, the implementer may opt for amainly software implementation; or, yet again alternatively, theimplementer may opt for some combination of hardware, software, and/orfirmware.

The foregoing detailed description has set forth various examples of thedevices and/or processes via the use of the block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it willunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples may be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, may be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g. as one or more programs running on one or more microprocessors),as firmware, or as virtually any combinations thereof, and thatdesigning the circuitry and/or writing the code for the software and/orfirmware would be well within the skill of one of skill in the art inlight of this disclosure.

The present disclosure is not to be limited in terms of the particularexamples described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its spirit and scope, as will be apparentto those skilled in the art. Functionally equivalent methods andapparatuses within the scope of the disclosure, in addition to thoseenumerated herein, will be apparent to those skilled in the art from theforegoing descriptions. Such modifications and variations are intendedto fall within the scope of the appended claims. The present disclosureis to be limited only by the terms of the appended claims, along withthe full scope of equivalents to which such claims are entitled. It isto be understood that this is disclosure is not limited to particularmethods, reagents, compounds compositions or biological systems, whichcan, of course, vary. It is also to be understood that the terminologyused herein is for the purpose of describing particular embodimentsonly, and is not intended to be limiting.

In addition, those skilled in the art will appreciate that themechanisms of the subject matter described herein are capable of beingdistributed as a program product in a variety of forms, and that anillustrative embodiment of the subject matter described herein appliesregardless of the particular type of signal bearing medium used toactually carry out the distribution. Examples of a signal bearing mediuminclude, but are not limited to, the following: a recordable type mediumsuch as floppy disk, a hard disk drive, a Compact Disc (CD), a DigitalVersatile Disk (DVD), a digital tape, a computer memory, a solid statedrive, etc.; and a transmission type medium such as a digital and/or ananalog communication medium (e.g., a fiber optic cable, a waveguide, awired communications link, a wireless communication link, etc.).

Those skilled in the art will recognize that it is common within the artto describe devices and/or processes in the fashion set forth herein,and thereafter use engineering practices to integrate such describeddevices and/or processes into data processing systems That is, at leasta portion of the devices and/or processes described herein may beintegrated into a data processing system via a reasonable amount ofexperimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity of gantry systems; control motors formoving and/or adjusting components and/or quantities).

A typical data processing system may be implemented utilizing anysuitable commercially available components, such as those typicallyfound in data computing/communication and/or networkcomputing/communication systems. The herein described subject mattersometimes illustrates different components contained within, orconnected with, different other components. It is to be understood thatsuch depicted architectures are merely exemplary, and that in fact manyother architectures may be implemented which achieve the samefunctionality. In a conceptual sense, any arrangement of components toachieve the same functionality is effectively “associated” such that thedesired functionality is achieved. Hence, any two components hereincombined to achieve a particular functionality may be seen as“associated with” each other such that the desired functionality isachieved, irrespective of architectures or intermediate components.Likewise, any two components so associated may also be viewed as being“operably connected”, or “operably coupled”, to each other to achievethe desired functionality, and any two components capable of being soassociated may also be viewed as being “operably couplable”, to eachother to achieve the desired functionality. Specific examples ofoperably couplable include but are not limited to physically connectableand/or physically interacting components and/or wirelessly interactableand/or wirelessly interacting components and/or logically interactingand/or logically interactable components.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to examples containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations. In addition, even if a specificnumber of an introduced claim recitation is explicitly recited, thoseskilled in the art will recognize that such recitation should beinterpreted to mean at least the recited number (e.g., the barerecitation of “two recitations,” without other modifiers, means at leasttwo recitations, or two or more recitations).

Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” is used, in general such a constructionis intended in the sense one having skill in the art would understandthe convention (e.g., “a system having at least one A, B, and C” wouldinclude but not be limited to systems that have A alone, B alone, Calone, A and B together, A and C together, B and C together, and/or A,B, and C together, etc.). It will be further understood by those withinthe art that virtually any disjunctive word and/or phrase presenting twoor more alternative terms, whether in the description, claims, ordrawings, should be understood to contemplate the possibilities ofincluding one of the terms, either of the terms, or both terms. Forexample, the phrase “A or B” will be understood to include thepossibilities of “A” or “B” or “A and B.”

In addition, where features or aspects of the disclosure are describedin terms of Markush groups, those skilled in the art will recognize thatthe disclosure is also thereby described in terms of any individualmember of subgroup of members of the Markush group.

As will be understood by one skilled in the art, for any and allpurposes, such as in terms of providing a written description, allranges disclosed herein also encompass any and all possible subrangesand combinations of subranges thereof. Any listed range can be easilyrecognized as sufficiently describing and enabling the same range beingbroken down into at least equal halves, thirds, quarters, fifths,tenths, etc. As a non-limiting example, each range discussed herein canbe readily broken down into a lower third, middle third and upper third,etc. As will also be understood by one skilled in the art all languagesuch as “up to,” “at least,” “greater than,” “less than,” and the likeinclude the number recited and refer to ranges which can be subsequentlybroken down into subranges as discussed above. Finally, as will beunderstood by one skilled in the art, a range includes each individualmember. Thus, for example, a group having 1-3 cells refers to groupshaving 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers togroups having 1, 2, 3, 4, or 5 cells, and so forth.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A method executed on a computing device tooptimize an efficiency of camera placement, numbers of cameras, andresolution associated with each of the cameras in a multi-cameramonitoring system, the method comprising: determining a maximumresolution matrix, wherein elements of the maximum resolution matrixrepresent a maximum resolution with which each of the cameras arecapable to monitor a distinct coverage area along a linear axis in themulti-camera monitoring system, and wherein the distinct coverage areais defined by a length of coverage along the linear axis in themulti-camera monitoring system; receiving a desired resolution vector,wherein each element of the desired resolution vector represents adesired resolution for each of the cameras in the multi-cameramonitoring system; determining whether any of the cameras violate aresolution consistency with one or more other cameras while monitoringthe distinct coverage area in the multi-camera monitoring system;excluding any of the cameras from the multi-camera monitoring systemthat violate the resolution consistency; and performing a discreteoptimization to determine an optimal camera from the cameras with areduced resolution error by: evaluating the elements of the maximumresolution matrix in view of the desired resolution vector to: compute acost function to scan the coverage area for each of the cameras as aproduct of the desired resolution for each of the cameras and the lengthof coverage along the linear axis in the multi-camera monitoring system;and determine the optimal camera from the cameras and an optimalresolution based on the cost function, wherein the cost functionincludes at least a resolution error that is assigned to each of thecameras; and minimizing the assigned resolution error by: switching on acamera in response to a detection of a first resolution coverage of thecameras; and switching off the camera in response to a detection of asecond resolution coverage of the cameras, wherein the first resolutioncoverage of the cameras is greater than the second resolution coverageof the cameras.
 2. The method according to claim 1, further comprising:evaluating the elements of the maximum resolution matrix in view of aweighting parameter for the cost function.
 3. The method according toclaim 2, further comprising: linearly combining a number of a cost ofthe cameras and a resolution cost error with the weighting parameter todetermine a total cost.
 4. The method according to claim 3, furthercomprising: minimizing the total cost to determine the optimal cameraand the optimal resolution for each point in the multi-camera monitoringsystem.
 5. A computing device operable to optimize an efficiency ofcamera placement, numbers of cameras, and a resolution associated witheach of the cameras in a multi-camera monitoring system, the computingdevice comprising: a memory configured to store instructions; an inputdevice configured to receive a desired resolution vector, whereinelements of the desired resolution vector represent a desired resolutionfor each of the cameras in the multi-camera monitoring system; and aprocessor configured to: determine a maximum resolution matrix, whereineach element of the maximum resolution matrix represents a maximumresolution with which each of the cameras are capable to monitor adistinct coverage area along a linear axis in the multi-cameramonitoring system, and wherein the distinct coverage area is defined bya length of coverage along the linear axis in the multi-cameramonitoring system; determine whether any of the cameras violate aresolution consistency with one or more other cameras while monitoringthe distinct coverage area in the multi-camera monitoring system;exclude any of the cameras from the multi-camera monitoring system thatviolate the resolution consistency; and perform a discrete optimizationto determine an optimal camera from the cameras with a reducedresolution error by a process to: evaluate the elements of the maximumresolution matrix in view of the desired resolution vector to: compute acost function to scan the coverage area for each of the cameras as aproduct of the desired resolution for each of the cameras and the lengthof coverage along the linear axis in the multi-camera monitoring system;and determine the optimal camera from the cameras and an optimalresolution based on the cost function, wherein the cost functionincludes at least a resolution error that is assigned to each of thecameras; and minimize the assigned resolution error by a process to:switch on a camera in response to a detection of a first resolutioncoverage of the cameras; and switch off the camera in response to adetection of a second resolution coverage of the cameras, wherein thefirst resolution coverage of the cameras is greater than the secondresolution coverage of the cameras.
 6. The computing device according toclaim 5, wherein the processor is further configured to: employ a greedytechnique to minimize a total cost iteratively.
 7. The computing deviceaccording to claim 6, wherein the processor is further configured to:select another camera from the cameras and assign another resolution tothe other camera from the cameras such that the other camera and theother resolution impose a largest reduction on the total cost at eachiteration; and continue the iterations until no other camera from thecameras or resolution selection decreases the cost function.
 8. Thecomputing device according to claim 5, wherein each resolutionrepresents a level of zoom.
 9. The method of claim 1, furthercomprising: employing a greedy technique to minimize a total costiteratively; selecting another camera from the cameras and assigninganother resolution to the other camera from the cameras such that theother camera and the other resolution impose a largest reduction on thetotal cost at each iteration; and continuing the iterations until noother camera or resolution selection decreases his cost function. 10.The method of claim 1, wherein each of the cameras includes one of: asingle resolution camera, a multi-resolution camera, a multidirectionalcamera, a multi-resolution/multidirectional camera, and a pan-tilt-zoom(PTZ) camera.
 11. The computing device of claim 5, wherein the processoris further configured to: evaluate the elements of the maximumresolution matrix in view of a weighting parameter for the costfunction; and linearly combine a number of a cost of each of the camerasand a resolution cost error with the weighting parameter to determine atotal cost.
 12. The computing device of claim 11, wherein the processoris further configured to: minimize the total cost to determine theoptimal camera and the optimal resolution for each point in themulti-camera monitoring system.